
"""simple docstring"""


from typing import Any, Dict, List, Optional, Tuple, Union

import torch
from torch import nn
from torch.utils.data import DistributedSampler, RandomSampler

from transformers import PreTrainedModel, Trainer, logging
from transformers.integrations import is_fairscale_available
from transformers.models.fsmt.configuration_fsmt import FSMTConfig
from transformers.optimization import (
    Adafactor,
    AdamW,
    get_constant_schedule,
    get_constant_schedule_with_warmup,
    get_cosine_schedule_with_warmup,
    get_cosine_with_hard_restarts_schedule_with_warmup,
    get_linear_schedule_with_warmup,
    get_polynomial_decay_schedule_with_warmup,
)
from transformers.trainer_pt_utils import get_tpu_sampler
from transformers.training_args import ParallelMode
from transformers.utils import is_torch_tpu_available


if is_fairscale_available():
      from fairscale.optim import OSS


SCREAMING_SNAKE_CASE			  =  logging.get_logger(__name__)

SCREAMING_SNAKE_CASE			  =  {
    """linear""": get_linear_schedule_with_warmup,
    """cosine""": get_cosine_schedule_with_warmup,
    """cosine_w_restarts""": get_cosine_with_hard_restarts_schedule_with_warmup,
    """polynomial""": get_polynomial_decay_schedule_with_warmup,
    """constant""": get_constant_schedule,
    """constant_w_warmup""": get_constant_schedule_with_warmup,
}



class 	__a	(   _lowerCAmelCase						):






       def __init__(	self			:				Optional[int]							,   UpperCAmelCase_			:				str=None							,   UpperCAmelCase_			:				Union[str, Any]=None							,   *UpperCAmelCase_			:				List[str]							,   **UpperCAmelCase_			:				Tuple      )->	Any:







              """simple docstring"""
              super().__init__(*UpperCAmelCase_							,   **UpperCAmelCase_      )

              if config is None:
                     assert isinstance(self.model							,   UpperCAmelCase_      ), (
                         "If no `config` is passed the model to be trained has to be of type `PreTrainedModel`, but is"
                         f" {self.model.__class__}"
                     )
                     UpperCamelCase       			=		self.model.config
              else:
                     UpperCamelCase       			=		config

              UpperCamelCase       			=		data_args
              UpperCamelCase       			=		self.config.tgt_vocab_size if isinstance(self.config							,   UpperCAmelCase_      ) else self.config.vocab_size

              if self.args.label_smoothing != 0 or (self.data_args is not None and self.data_args.ignore_pad_token_for_loss):
                     assert self.config.pad_token_id is not None, (
                         "Make sure that `config.pad_token_id` is correcly defined when ignoring `pad_token` for loss"
                         " calculation or doing label smoothing."
                     )

              if self.config.pad_token_id is None and self.config.eos_token_id is not None:
                     logger.warning(
                         f"The `config.pad_token_id` is `None`. Using `config.eos_token_id` = {self.config.eos_token_id} for"
                         " padding.."      )

              if self.args.label_smoothing == 0:
                     UpperCamelCase       			=		torch.nn.CrossEntropyLoss(ignore_index=self.config.pad_token_id      )
              else:
                     # dynamically import label_smoothed_nll_loss
                     from utils import label_smoothed_nll_loss

                     UpperCamelCase       			=		label_smoothed_nll_loss






       def 						_SCREAMING_SNAKE_CASE							(	self			:				Union[str, Any]							,   UpperCAmelCase_			:				int      )->	str:







              """simple docstring"""
              if self.optimizer is None:
                     UpperCamelCase       			=		["bias", "LayerNorm.weight"]
                     UpperCamelCase       			=		[
                         {
                             "params": [p for n, p in self.model.named_parameters() if not any(nd in n for nd in no_decay      )],
                             "weight_decay": self.args.weight_decay,
                         },
                         {
                             "params": [p for n, p in self.model.named_parameters() if any(nd in n for nd in no_decay      )],
                             "weight_decay": 0.0,
                         },
                     ]
                     UpperCamelCase       			=		Adafactor if self.args.adafactor else AdamW
                     if self.args.adafactor:
                            UpperCamelCase       			=		Adafactor
                            UpperCamelCase       			=		{"scale_parameter": False, "relative_step": False}
                     else:
                            UpperCamelCase       			=		AdamW
                            UpperCamelCase       			=		{
                                "betas": (self.args.adam_betaa, self.args.adam_betaa),
                                "eps": self.args.adam_epsilon,
                            }
                     UpperCamelCase       			=		self.args.learning_rate
                     if self.sharded_ddp:
                            UpperCamelCase       			=		OSS(
                                params=UpperCAmelCase_							,   optim=UpperCAmelCase_							,   **UpperCAmelCase_							,   )
                     else:
                            UpperCamelCase       			=		optimizer_cls(UpperCAmelCase_							,   **UpperCAmelCase_      )

              if self.lr_scheduler is None:
                     UpperCamelCase       			=		self._get_lr_scheduler(UpperCAmelCase_      )
              else:  # ignoring --lr_scheduler
                     logger.warning("scheduler is passed to `Seq2SeqTrainer`, `--lr_scheduler` arg is ignored."      )






       def 						_SCREAMING_SNAKE_CASE							(	self			:				List[Any]							,   UpperCAmelCase_			:				str      )->	Any:







              """simple docstring"""
              UpperCamelCase       			=		arg_to_scheduler[self.args.lr_scheduler]
              if self.args.lr_scheduler == "constant":
                     UpperCamelCase       			=		schedule_func(self.optimizer      )
              elif self.args.lr_scheduler == "constant_w_warmup":
                     UpperCamelCase       			=		schedule_func(self.optimizer							,   num_warmup_steps=self.args.warmup_steps      )
              else:
                     UpperCamelCase       			=		schedule_func(
                         self.optimizer							,   num_warmup_steps=self.args.warmup_steps							,   num_training_steps=UpperCAmelCase_      )
              return scheduler






       def 						_SCREAMING_SNAKE_CASE							(	self			:				str      )->	Optional[torch.utils.data.Sampler]:







              """simple docstring"""
              if isinstance(self.train_dataset							,   torch.utils.data.IterableDataset      ):
                     return None
              elif is_torch_tpu_available():
                     return get_tpu_sampler(self.train_dataset      )
              else:
                     if self.args.sortish_sampler:
                            self.train_dataset.make_sortish_sampler(
                                self.args.per_device_train_batch_size							,   distributed=(self.args.parallel_mode == ParallelMode.DISTRIBUTED)							,   )

                     return (
                         RandomSampler(self.train_dataset      )
                         if self.args.local_rank == -1
                         else DistributedSampler(self.train_dataset      )
                     )






       def 						_SCREAMING_SNAKE_CASE							(	self			:				List[Any]							,   UpperCAmelCase_			:				int							,   UpperCAmelCase_			:				int							,   UpperCAmelCase_			:				int      )->	Optional[int]:







              """simple docstring"""
              if self.args.label_smoothing == 0:
                     if self.data_args is not None and self.data_args.ignore_pad_token_for_loss:
                            # force training to ignore pad token
                            UpperCamelCase       			=		model(**UpperCAmelCase_							,   use_cache=UpperCAmelCase_      )[0]
                            UpperCamelCase       			=		self.loss_fn(logits.view(-1							,   logits.shape[-1]      )							,   labels.view(-1      )      )
                     else:
                            # compute usual loss via models
                            UpperCamelCase			,							UpperCamelCase       			=		model(**UpperCAmelCase_							,   labels=UpperCAmelCase_							,   use_cache=UpperCAmelCase_      )[:2]
              else:
                     # compute label smoothed loss
                     UpperCamelCase       			=		model(**UpperCAmelCase_							,   use_cache=UpperCAmelCase_      )[0]
                     UpperCamelCase       			=		torch.nn.functional.log_softmax(UpperCAmelCase_							,   dim=-1      )
                     UpperCamelCase			,							UpperCamelCase       			=		self.loss_fn(UpperCAmelCase_							,   UpperCAmelCase_							,   self.args.label_smoothing							,   ignore_index=self.config.pad_token_id      )
              return loss, logits






       def 						_SCREAMING_SNAKE_CASE							(	self			:				List[str]							,   UpperCAmelCase_			:				Dict							,   UpperCAmelCase_			:				List[Any]      )->	Union[str, Any]:







              """simple docstring"""
              UpperCamelCase       			=		inputs.pop("labels"      )
              UpperCamelCase			,							UpperCamelCase       			=		self._compute_loss(UpperCAmelCase_							,   UpperCAmelCase_							,   UpperCAmelCase_      )
              return loss






       def 						_SCREAMING_SNAKE_CASE							(	self			:				List[str]							,   UpperCAmelCase_			:				nn.Module							,   UpperCAmelCase_			:				Dict[str, Union[torch.Tensor, Any]]							,   UpperCAmelCase_			:				bool							,   UpperCAmelCase_			:				Optional[List[str]] = None							,   )->	Tuple[Optional[float], Optional[torch.Tensor], Optional[torch.Tensor]]:







              """simple docstring"""
              UpperCamelCase       			=		self._prepare_inputs(UpperCAmelCase_      )

              UpperCamelCase       			=		{
                  "max_length": self.data_args.val_max_target_length
                  if self.data_args is not None
                  else self.config.max_length,
                  "num_beams": self.data_args.eval_beams if self.data_args is not None else self.config.num_beams,
              }

              if self.args.predict_with_generate and not self.args.prediction_loss_only:
                     UpperCamelCase       			=		self.model.generate(
                         inputs["input_ids"]							,   attention_mask=inputs["attention_mask"]							,   **UpperCAmelCase_							,   )
                     # in case the batch is shorter than max length, the output should be padded
                     if generated_tokens.shape[-1] < gen_kwargs["max_length"]:
                            UpperCamelCase       			=		self._pad_tensors_to_max_len(UpperCAmelCase_							,   gen_kwargs["max_length"]      )

              UpperCamelCase       			=		inputs.pop("labels"      )
              with torch.no_grad():
                     # compute loss on predict data
                     UpperCamelCase			,							UpperCamelCase       			=		self._compute_loss(UpperCAmelCase_							,   UpperCAmelCase_							,   UpperCAmelCase_      )

              UpperCamelCase       			=		loss.mean().detach()
              if self.args.prediction_loss_only:
                     return (loss, None, None)

              UpperCamelCase       			=		generated_tokens if self.args.predict_with_generate else logits

              if labels.shape[-1] < gen_kwargs["max_length"]:
                     UpperCamelCase       			=		self._pad_tensors_to_max_len(UpperCAmelCase_							,   gen_kwargs["max_length"]      )

              return (loss, logits, labels)






       def 						_SCREAMING_SNAKE_CASE							(	self			:				List[str]							,   UpperCAmelCase_			:				Union[str, Any]							,   UpperCAmelCase_			:				Dict      )->	int:







              """simple docstring"""
              # If PAD token is not defined at least EOS token has to be defined
              UpperCamelCase       			=		self.config.pad_token_id if self.config.pad_token_id is not None else self.config.eos_token_id

              if pad_token_id is None:
                     raise ValueError(
                         "Make sure that either `config.pad_token_id` or `config.eos_token_id` is defined if tensor has to be"
                         f" padded to `max_length`={max_length}"      )

              UpperCamelCase       			=		pad_token_id * torch.ones(
                  (tensor.shape[0], max_length)							,   dtype=tensor.dtype							,   device=tensor.device      )
              UpperCamelCase       			=		tensor
              return padded_tensor







"""simple docstring"""


SCREAMING_SNAKE_CASE			  =  {}



def 						lowerCamelCase__      (				UpperCAmelCase_		,   UpperCAmelCase_		,   UpperCAmelCase_					)->				int:







       """simple docstring"""

       # if we are absent twice, or late 3 consecutive days,
       # no further prize strings are possible
       if late == 3 or absent == 2:
              return 0

       # if we have no days left, and have not failed any other rules,
       # we have a prize string
       if days == 0:
              return 1

       # No easy solution, so now we need to do the recursive calculation

       # First, check if the combination is already in the cache, and
       # if yes, return the stored value from there since we already
       # know the number of possible prize strings from this point on
       UpperCamelCase       			=		(days, absent, late)
       if key in cache:
              return cache[key]

       # now we calculate the three possible ways that can unfold from
       # this point on, depending on our attendance today

       # 1) if we are late (but not absent), the "absent" counter stays as
       # it is, but the "late" counter increases by one
       UpperCamelCase       			=		_calculate(days - 1		,   UpperCAmelCase_		,   late + 1					)

       # 2) if we are absent, the "absent" counter increases by 1, and the
       # "late" counter resets to 0
       UpperCamelCase       			=		_calculate(days - 1		,   absent + 1		,   0					)

       # 3) if we are on time, this resets the "late" counter and keeps the
       # absent counter
       UpperCamelCase       			=		_calculate(days - 1		,   UpperCAmelCase_		,   0					)

       UpperCamelCase       			=		state_late + state_absent + state_ontime

       UpperCamelCase       			=		prizestrings
       return prizestrings



def 						lowerCamelCase__      (				UpperCAmelCase_ = 30					)->				int:







       """simple docstring"""
       return _calculate(UpperCAmelCase_		,   absent=0		,   late=0					)


if __name__ == "__main__":
      print(solution())





